A Smartphone App for Improvement of Colonoscopy Preparation (ColoprAPP): Development and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Optimal bowel preparation is one of the major cornerstones for quality of colonoscopy. But poor bowel preparation still occurs in 10% to 25% of all patients. To optimize patient guidance, we developed a new smartphone app (ColoprAPP) for Android smartphones which guides and accompanies the patient starting 4 days before colonoscopy throughout the whole colonoscopy preparation procedure. OBJECTIVE: The objective of this study was to assess the function of a newly developed smartphone app for supporting colonoscopy preparation. METHODS: We carried out a prospective feasibility study including 25 patients undergoing outpatient colonoscopy at our hospital. As a control, we retrieved the data of 25 patients undergoing outpatient colonoscopy matching in age, sex, and indication for colonoscopy from our colonoscopy database. Patients were asked to download the smartphone app, ColoprAPP, in addition to being given the regular colonoscopy preparation leaflet. All colonoscopies were performed in the morning after using a split-dose preparation containing a polyethlene glycol-based purgative. The study was designed to test feasibility of the prototype, evaluate grade of bowel cleanliness (Boston bowel preparation scale [BBPS]), and assess patient satisfaction with the app. RESULTS: The smartphone app use was feasible in all patients. BBPS count as a marker for grade of bowel preparation was significantly higher in the smartphone app-supported group (mean 8.1 [SD 0.3] vs 7.1 [SD 0.4], P=.02). Left (mean 2.8 [SD 0.1] vs 2.4 [SD 0.11], P=.02) and transverse colon (mean 2.8 [SD 0.07] vs 2.4 [SD 0.11], P<.001) revealed significantly higher BBPS counts in the smartphone app-supported group than in controls. Patient satisfaction with a smartphone app-supported colonoscopy preparation was high with an average numeric rating scale score for usefulness of 8.2 (visual analog scale 1-10). CONCLUSIONS: A novel developed smartphone app for reinforced education of bowel cleansing was feasible and led to high BBPS scores and patient satisfaction. TRIAL REGISTRATION: ClinicalTrials.gov NCT02512328; https://clinicaltrials.gov/ct2/show/NCT02512328 (Archived by WebCite at http://www.webcitation.org/6sz3Kk26z).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".